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. 2023 Jun 27;13(1):10440.
doi: 10.1038/s41598-023-36798-7.

Initial study on an expert system for spine diseases screening using inertial measurement unit

Mariusz Pelc #  1   2 , Radana Vilimkova Kahankova #  3 , Monika Blaszczyszyn #  4 , Dariusz Mikolajewski #  5 , Mariusz Konieczny  4 , Volodymir Khoma  6   7 , Gregor Bara #  8 , Jaroslaw Zygarlicki #  6 , Radek Martinek  6   3 , Munish K Gupta #  9   10 , Edward Jacek Gorzelanczyk  11   12   13   14   15 , Mateusz Pawłowski  16   17 , Bogdan Czapiga  18 , Malgorzata Zygarlicka  6 , Aleksandra Kawala-Sterniuk #  19
Affiliations

Initial study on an expert system for spine diseases screening using inertial measurement unit

Mariusz Pelc et al. Sci Rep. .

Abstract

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
IMU unit.
Figure 2
Figure 2
Summary of the the device software components functional interaction.
Figure 3
Figure 3
IMU placement.
Figure 4
Figure 4
Example policy.
Figure 5
Figure 5
Example template.
Figure 6
Figure 6
Example action.
Figure 7
Figure 7
Example rule.
Figure 8
Figure 8
Example ToleranceRangeCheck.
Figure 9
Figure 9
Example UtilityFunction.
Figure 10
Figure 10
Example declaration of ExternalVariables.
Figure 11
Figure 11
Example declaration of InternalVariables.
Figure 12
Figure 12
Example Declaration of ReturnValues.
Figure 13
Figure 13
Definition of linguistic variables example.
Figure 14
Figure 14
Definition of membership functions example.
Figure 15
Figure 15
Definition of membership functions example.
Figure 16
Figure 16
Actual definition of the EnvironmentVariables.
Figure 17
Figure 17
Threshold values initialisation using Template object.
Figure 18
Figure 18
Threshold values initialisation using Template object.
Figure 19
Figure 19
Trace showing policy execution for Amplitude = 1.6 and Width = 105.
Figure 20
Figure 20
Policy changed to re-define the state variables values.
Figure 21
Figure 21
Trace showing policy execution for Amplitude = 0.6 and Width = 300.
Figure 22
Figure 22
Recordings from the sensors on the head—X Axis.
Figure 23
Figure 23
Recordings from the sensors on the head—Y Axis.
Figure 24
Figure 24
Recordings from the sensors on the head—Z Axis.
Figure 25
Figure 25
Recordings from the sensors on the torso—X Axis.
Figure 26
Figure 26
Recordings from the sensors on the torso—Y Axis.
Figure 27
Figure 27
Recordings from the sensors on the torso—Z Axis.
Figure 28
Figure 28
Trace showing policy execution for Amplitude = 1.6 and Width = 105.
Figure 29
Figure 29
Trace showing policy execution for Amplitude = 1.6 and Width = 105.
See this image and copyright information in PMC

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